Neural Network Modeling of CuO/Au Hybrid Nanofluid Thermal Performance with Slip Effects for Advanced Process Applications

This study explores transient magnetohydrodynamic (MHD) heat and mass transfer in the flow of a hybrid nanofluid over a stretching surface, considering both steady and unsteady scenarios. The investigation incorporates chemical reactions, slip boundary conditions, and the effects of thermal radiation. The hybrid nanofluid, composed of copper oxide (CuO) and gold (Au) nanoparticles in a water-based fluid, demonstrates enhanced thermal performance compared with base fluids. Key findings reveal that higher nanoparticle concentrations significantly improve heat transfer, highlighting the potential of hybrid nanofluids in advanced thermal management applications. Additionally, machine learning models effectively predict heat transfer characteristics with high accuracy (R2 = 0.99), showcasing their effectiveness in complementing traditional numerical methods. These findings contribute to the understanding of hybrid nanofluids in complex thermal systems and highlight the utility of emerging computational tools for thermal analysis.